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"... Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions. T ..."

Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions. These and previously proposed estimators of cointegrating vectors are used to study long-run U.S. money (Ml) demand. Ml demand is found to be stable over 1900-1989; the 95 % confidence intervals for the income elasticity and interest rate semielasticity are (.88,1.06) and (-.13,-.08), respectively. Estimates based on the postwar data alone, however, are unstable, with variances which indicate substantial sampling uncertainty.

"... This paper provides a survey and review of the major econometric work on long memory processes, fractional integration, and their applications in economics and finance. Some of the definitions of long memory are reviewed, together with previous work in other disciplines. Section 3 describes the popu ..."

This paper provides a survey and review of the major econometric work on long memory processes, fractional integration, and their applications in economics and finance. Some of the definitions of long memory are reviewed, together with previous work in other disciplines. Section 3 describes the population characteristics of various long memory processes in the mean, including ARFIMA. Section 4 is concerned with estimation and examines emiparametric procedures in both *he frequency and time domain, and also the properties of various regression based and maximum likelihood techniques. Long memory volatility processes are discussed in Section 5, while Section 6 discusses applications in economics and finance. The paper also has a concluding section.

"... This paper investigates the predictions of a simple optimizing model of nominal price rigidity for the aggregate price level and the dynamics of inflation. I compare the model’s predictions with those of a perfectly competitive, flexible price ‘benchmark’ model (corresponding to the model of pricing ..."

This paper investigates the predictions of a simple optimizing model of nominal price rigidity for the aggregate price level and the dynamics of inflation. I compare the model’s predictions with those of a perfectly competitive, flexible price ‘benchmark’ model (corresponding to the model of pricing assumed in standard real business cycle models), and evaluate how much the introduction of nominal rigidities improves the model’s fit with the data. The model’s predictions are derived using only the firms optimal pricing problem; taking as given the paths of nominal labor compensation, labor productivity, and output, I determine the implied path of prices predicted by the model. Because prices are not a stationary series, I present my results in terms of the predicted path of the price/unit labor cost ratio, where the parameters characterizing such paths are chosen to maximize the fit with the data. I find that, while the evolution of prices relative to unit labor costs is quite different from what would be predicted by the flexible-price ‘benchmark ’ model, a simple model of nominal price rigidity delivers an extremely close approximation both of the price/unit labor cost ratio and of the inflation series, even under a very simple approach to the measurement of marginal costs. Moreover, the results are robust to modifications of this measure.

... allow us to shed new light on earlier claims that stocksprices are too volatile to accord with such models (LeRoy and Porter [14], Shillers[20], Mankiw, Romer, and Shapiro [15], Campbell and Shiller =-=[1, 2]-=-, and Wests[23]).sIt seems appropriate to consider earnings data for forecasting dividends, sincesearnings are constructed by accountants with the objective of helping people tosevaluate the fundament...

"... you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact inform ..."

you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at

Abstract: Long-horizon regression tests are widely used in empirical finance, despite evidence of severe size distortions. I propose a new bootstrap method for small-sample inference in long-horizon regressions. A Monte Carlo study shows that this bootstrap test greatly reduces the size distortions of conventional long-horizon regression tests. I also find that long-horizon regression tests do not have power advantages against economically plausible alternatives. The apparent lack of higher power at long horizons suggests that previous findings of increasing long-horizon predictability are more likely due to size distortions than to power gains. I illustrate the use of the bootstrap method by analyzing whether monetary fundamentals help predict changes in four major exchange rates. In contrast to earlier studies, I find only weak evidence of exchange rate predictability and no evidence of increasing long-horizon predictability. Many of the differences in results can be traced to the implementation of the test.

"... This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the ..."

This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weak-identification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo studies. Confidence sets immune to weak identification are proposed. We use these results to inform an empirical investigation of various CCAPM specifications; the substantive conclusions reached differ from those obtained using conventional methods.